code: def convert_tensorflow_model(
      model_name,
      model_ckpt,
      output_file,
      example_img="misc/panda.jpg",
      weights_only=True,
  ):
      """Loads and saves a TensorFlow model."""
      image_files = [example_img]
      eval_ckpt_driver = eval_ckpt_main.EvalCkptDriver(model_name)
      with tf.Graph().as_default(), tf.Session() as sess:
          images, _ = eval_ckpt_driver.build_dataset(
              image_files, [0] * len(image_files), False
          )
          eval_ckpt_driver.build_model(images, is_training=False)
          sess.run(tf.global_variables_initializer())
          eval_ckpt_driver.restore_model(sess, model_ckpt)
          global_variables = tf.global_variables()
          weights = dict()
          for variable in global_variables:
              try:
                  weights[variable.name] = variable.eval()
              except:
                  print(
                      f"Skipping variable {variable.name}, an exception occurred"
                  )
      model = _get_model_by_name(
          model_name,
          include_top=True,
          input_shape=None,
          weights=None,
          classes=1000,
      )
      load_weights(model, weights)
      output_file = f"{output_file}.h5"
      if weights_only:
          model.save_weights(output_file)
      else:
          model.save(output_file)
  
file_path: efficientnet/scripts/load_efficientnet.py
imports: import argparse
  import sys
  import numpy
  import tensorflow
  import efficientnet.keras
  from keras.layers import BatchNormalization, Conv2D, Dense
label_desc: Use image node_id 4 for calling a global function with example usage: convert_tensorflow_model(model_name, model_ckpt, output_file, example_img, weights_only) without return types